To start, it is important to understand that session time tracking and other Google Analytics time tracking in general are not always entirely accurate in the way we believe. Another way to explain the way time tracking works is that reports are essentially always accurate, but may not be tracking in the way we believe they are tracking. To define it succinctly, session time is the amount of time from the start of a unique visit to the last record-able time stamp the visitor activated. This definition however, is broad and means very little without additional information on a unique visit and time stamps.
A unique visit depends on how a person got to your site and how long you have been away from/idle on the site. By default sessions timeout after 30 minutes of inactivity. The timeout length can be manually changed in your Analytics code. Being inactive means you either have not moved onto a new page, or triggered an event within the session timeout period. If you return to the site after a session timeout, then you will be counted as a unique visit. The second way to make a unique visit is to come to the site that is tracked by a different Google Analytics campaign. Say your site sells garden plants. Someone searches for ‘blue flowers’ and clicks a link to your site. This will make a session and start calculating how long the visitor spends on your site. Say the same visitor opens a new tab and searches for ‘lemon tree’ and clicks another link to your site. Supposing ‘lemon tree’ fell into a different campaign then this will count as a different session and be tracked separate of the last session.
Time stamps are a tricky business to handle and largely depend on how you set up tracking on your site. Lets assume the analytics tracking on your site is rather basic, the minimum required to track sessions. In this scenario the session time would be (Time Spent on All Pages – Time Spent on Last Page).
An example: someone visits the first page for 30 seconds, then spends 2 minutes on the next page, and lastly 20 minutes on the next and final page they browse. These page times would occur in a situation where someone goes to your home page, clicks on your articles link and browses an article, then finds one they like and reads it, closing the tab after they finish.
P1 = :30, P2 = 2:00, P3 = 20:00
Using our previous formula (Time Spent on All Pages – Time Spent on Last Page):
(P1 + P2 + P3) – P3 = 2:30
As far as Google Analytics is concerned, this user only spent two minutes and thirty seconds on your site. This is because with the basic tracking setup Google uses the time stamps from when a page loads to calculate time. The formula would look something like this:
P(x) = P(x+1) – P(x) where x is the next page visited. Since there is no next page when you are on the last page, Google Analytics cannot calculate the time spent on the page.
With a more advanced tracking setup that includes tracking for on page events you can more accurately account for the time a user actually spends on your page. With the same example above you add event tracking for when someone scrolls down 4 lines on a page and/or reaches the end of a page. In this case Google gets the time stamps for when someone loads a new page, but they also get the time stamps from your events and can therefore get an idea of how long the visitor spent on the last page.
Lets say in the article the visitor was reading reached the end of the html page which contained the article, but continued reading for 30 seconds before closing the page. The caluclated time would look something like this:
p1 = P2 -P1, P2 = P3 – P2, P3 = P(last event) – P3
with P3 being calculated as the last event time stamp on P3 minus the time P3 was loaded.
P1 = :30, P2 = 2:00, P3 = 19:30
Time = 22:00
With more advanced tracking we have a better representation of how much time a unique visitor spent on your site. It is important to note that no matter how many variables for tracking are implemented, inaccuracies with the actual time of a visit can occur. Take into context how the data is being calculated and how it relates to your site as you analyze the information at hand.
View our other articles related to Google Analytics to learn more.
So brilliently explained. thanks
Aleena Joseph says
livia Mathews says
Nice and Informational